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Multivariate Analysis and Visualization Tools for Metabolomic Data

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Dmitry Grapov and Oliver Fiehn University of California, Davis Multivariate Analysis and Visualization Tools for Metabolomic Data


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State of the art facility producing massive amounts of biological data… >20-30K samples/yr >200 studies


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Data Analysis and Visualization Quality Assessment use replicated mesurements and/or internal standards to estimate analytical variance Statistical and Multivariate use the experimental design to test hypotheses and/or identify trends in analytes Functional use statistical and multivariate results to identify impacted biochemical domains Network integrate statistical and multivariate results with the experimental design and analyte metadata experimental design - organism, sex, age etc. analyte description and metadata - biochemical class, mass spectra, etc.


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Data Analysis and Visualization Quality Assessment use replicated mesurements and/or internal standards to estimate analytical variance Statistical and Multivariate use the experimental design to test hypotheses and/or identify trends in analytes Functional use statistical and multivariate results to identify impacted biochemical domains Network integrate statistical and multivariate results with the experimental design and analyte metadata Network Mapping experimental design - organism, sex, age etc. analyte description and metadata - biochemical class, mass spectra, etc.


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Data Quality Assessment Drift in >400 replicated measurements across >100 analytical batches for a single analyte Acquisition batch Abundance QCs embedded among >5,5000 samples (1:10) collected over 1.5 yrs If the biological effect size is less than the analytical variance then the experiment will incorrectly yield insignificant results


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Data Quality Assessment Analyte specific data quality overview Normalizations need to be numerically and visually validated


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Statistical and Multivariate Analyses


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Statistical and Multivariate Analyses To see the big picture it is necessary too view the data from multiple different angles


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DeviumWeb https://github.com/dgrapov/DeviumWeb visualization statistics clustering PCA O-PLS


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DeviumWeb https://github.com/dgrapov/DeviumWeb visualization statistics clustering PCA O-PLS


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Functional Analysis Nucl. Acids Res. (2008) 36 (suppl 2): W423-W426.doi: 10.1093/nar/gkn282


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Functional Analysis: opportunity for ‘Omic integration Use domain knowledge databases to integrate genomic, proteomic and metabolomic data Current approaches can be limited to pathway level analyses


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Networks Biochemical reaction domain Structural molecular fingerprints mass spectra Empirical correlation partial correlation


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Mapped Network - displaying metabolic differences in control vs. malignant lung tissue


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Empirical Networks Use experiment specific or data driven relationships to gain novel insight into biochemical relationships


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Mass Spectral Networks Use mass spectra as a proxy for structure to help make sense of unknown compounds’ biochemical identities Watrous J et al. PNAS 2012;109:E1743-E1752


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Mass Spectral Networks Use mass spectra and empirical relationships to narrow down the biochemical roles for unknown compounds Rigorous chemical experiments identified the unknown compounds as partial derivatization products of glucose


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MetaMapR https://github.com/dgrapov/MetaMapR


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Analysis at the Metabolomic Scale and Beyond Pathway independent metabolomic (known and unknown), proteomic and genomic data integration


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Software and Resources DeviumWeb- Dynamic multivariate data analysis and visualization platform url: https://github.com/dgrapov/DeviumWeb imDEV- Microsoft Excel add-in for multivariate analysis url: http://sourceforge.net/projects/imdev/ MetaMapR: Network analysis tools for metabolomics url: https://github.com/dgrapov/MetaMapR TeachingDemos- Tutorials and demonstrations url: http://sourceforge.net/projects/teachingdemos/?source=directory url: https://github.com/dgrapov/TeachingDemos Data analysis case studies and Examples url: http://imdevsoftware.wordpress.com/


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dgrapov@ucdavis.edu metabolomics.ucdavis.edu This research was supported in part by NIH 1 U24 DK097154


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